code: def default_collate_fn(
      batch: List[Mapping[str, Tensor]], opts: argparse.Namespace
  ) -> Mapping[str, Tensor]:
      """Combines a list of dictionaries into a single dictionary by concatenating matching fields.
  
      Args:
          batch: A list of dictionaries
          opts: An argparse.Namespace instance.
  
      Returns:
          A dictionary with the same keys as batch[0].
      """
      batch_size = len(batch)
  
      # get the keys for first element in the list, assuming all elements have the same keys
      keys = list(batch[0].keys())
  
      new_batch = {k: [] for k in keys}
      for b in range(batch_size):
          for k in keys:
              new_batch[k].append(batch[b][k])
  
      # stack the keys
      for k in keys:
          batch_elements = new_batch.pop(k)
  
          if isinstance(batch_elements[0], (int, float)):
              # list of ints or floats
              batch_elements = torch.as_tensor(batch_elements)
          else:
              # stack tensors (including 0-dimensional)
              try:
                  batch_elements = torch.stack(
                      batch_elements, dim=0
                  ).contiguous()
              except Exception as e:
                  logger.error(
                      "Unable to stack the tensors. Error: {}".format(e)
                  )
  
          new_batch[k] = batch_elements
  
      return new_batch
  
file_path: ml-cvnets/data/collate_fns/collate_functions.py
imports: import argparse
  from typing import Any, List, Mapping
  import torch
  from torch import Tensor
  from torch.utils.data import default_collate
  from data.collate_fns import COLLATE_FN_REGISTRY
  from utils import logger
label_desc: Use image node_id 4 for calling a global function with example usage: default_collate_fn(batch, opts) and returns: new_batch